论文标题
粒子物理的固定设定固定生成
Set-Conditional Set Generation for Particle Physics
论文作者
论文摘要
粒子物理数据的仿真是在大型强子对撞机上进行物理分析的基本但计算密集型成分，在该物理分析中，观察性设置值数据是在一组传入粒子上生成条件的。为了加速这项任务，我们提出了一个基于图神经网络和插槽注意成分的新颖生成模型，该模型超出了先前存在的基准的性能。
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
